RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods

Handle URI:
http://hdl.handle.net/10754/619769
Title:
RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods
Authors:
Germain, Pierre-Luc; Vitriolo, Alessandro; Adamo, Antonio ( 0000-0003-1080-3547 ) ; Laise, Pasquale; Das, Vivek; Testa, Giuseppe
Abstract:
RNA sequencing (RNAseq) has become the method of choice for transcriptome analysis, yet no consensus exists as to the most appropriate pipeline for its analysis, with current benchmarks suffering important limitations. Here, we address these challenges through a rich benchmarking resource harnessing (i) two RNAseq datasets including ERCC ExFold spike-ins; (ii) Nanostring measurements of a panel of 150 genes on the same samples; (iii) a set of internal, genetically-determined controls; (iv) a reanalysis of the SEQC dataset; and (v) a focus on relative quantification (i.e. across-samples). We use this resource to compare different approaches to each step of RNAseq analysis, from alignment to differential expression testing. We show that methods providing the best absolute quantification do not necessarily provide good relative quantification across samples, that count-based methods are superior for gene-level relative quantification, and that the new generation of pseudo-alignment-based software performs as well as established methods, at a fraction of the computing time. We also assess the impact of library type and size on quantification and differential expression analysis. Finally, we have created a R package and a web platform to enable the simple and streamlined application of this resource to the benchmarking of future methods.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division
Citation:
RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods 2016, 44 (11):5054 Nucleic Acids Research
Publisher:
Oxford University Press (OUP)
Journal:
Nucleic Acids Research
Issue Date:
20-Jun-2016
DOI:
10.1093/nar/gkw448
Type:
Article
ISSN:
0305-1048; 1362-4962
Sponsors:
European Research Council [616441 – DISEASEAVATARS to G.T.]; Regione Lombardia (Ricerca Indipendente 2012); Italian Ministry of Health (Ricerca Corrente to G.T.); ERA-NET Neuron Program (to G.T. and P.L.G.); Italian Association for Cancer Research (to G.T.); EPIGEN Flagship Project of the Italian National Research Council (to G.T.); Jerome-Lejeune Foundation (to G.T.); Umberto Veronesi Foundation (fellowship to P.L.G.); Federation of European Biochemical Societies (to A.A.); Italian Foundation for Cancer Research (to P.L. and V.D.). Funding for open access charge: ERC Research Grant DISEASEAVATARS [616441].
Additional Links:
http://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkw448
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorGermain, Pierre-Lucen
dc.contributor.authorVitriolo, Alessandroen
dc.contributor.authorAdamo, Antonioen
dc.contributor.authorLaise, Pasqualeen
dc.contributor.authorDas, Viveken
dc.contributor.authorTesta, Giuseppeen
dc.date.accessioned2016-09-04T08:12:11Z-
dc.date.available2016-09-04T08:12:11Z-
dc.date.issued2016-06-20-
dc.identifier.citationRNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods 2016, 44 (11):5054 Nucleic Acids Researchen
dc.identifier.issn0305-1048-
dc.identifier.issn1362-4962-
dc.identifier.doi10.1093/nar/gkw448-
dc.identifier.urihttp://hdl.handle.net/10754/619769-
dc.description.abstractRNA sequencing (RNAseq) has become the method of choice for transcriptome analysis, yet no consensus exists as to the most appropriate pipeline for its analysis, with current benchmarks suffering important limitations. Here, we address these challenges through a rich benchmarking resource harnessing (i) two RNAseq datasets including ERCC ExFold spike-ins; (ii) Nanostring measurements of a panel of 150 genes on the same samples; (iii) a set of internal, genetically-determined controls; (iv) a reanalysis of the SEQC dataset; and (v) a focus on relative quantification (i.e. across-samples). We use this resource to compare different approaches to each step of RNAseq analysis, from alignment to differential expression testing. We show that methods providing the best absolute quantification do not necessarily provide good relative quantification across samples, that count-based methods are superior for gene-level relative quantification, and that the new generation of pseudo-alignment-based software performs as well as established methods, at a fraction of the computing time. We also assess the impact of library type and size on quantification and differential expression analysis. Finally, we have created a R package and a web platform to enable the simple and streamlined application of this resource to the benchmarking of future methods.en
dc.description.sponsorshipEuropean Research Council [616441 – DISEASEAVATARS to G.T.]; Regione Lombardia (Ricerca Indipendente 2012); Italian Ministry of Health (Ricerca Corrente to G.T.); ERA-NET Neuron Program (to G.T. and P.L.G.); Italian Association for Cancer Research (to G.T.); EPIGEN Flagship Project of the Italian National Research Council (to G.T.); Jerome-Lejeune Foundation (to G.T.); Umberto Veronesi Foundation (fellowship to P.L.G.); Federation of European Biochemical Societies (to A.A.); Italian Foundation for Cancer Research (to P.L. and V.D.). Funding for open access charge: ERC Research Grant DISEASEAVATARS [616441].en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkw448en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.titleRNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methodsen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalNucleic Acids Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionEuropean Institute of Oncology, Department of Experimental Oncology, Via Adamello 16, 20139 Milano, Italyen
dc.contributor.institutionUniversity of Milan, Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, 20122 Milano, Italyen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorAdamo, Antonioen
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